code: def test_wrong_multilabel_prediction_format(
      text_multilabel_classification_dataset_mock,
  ):
      # Arrange
      emtpy_suite = Suite("Empty Suite")
  
      # Act & Assert
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_multilabel_classification_dataset_mock,
              train_predictions=[
                  [0, 0, 0],
                  [0, 0, 0],
                  [0, 0, 0],
                  [0, 0, 0],
              ],
          ),
          raises(
              ValidationError,
              "Check requires multi-label classification predictions for "
              'the "Train" dataset to have 3 rows, same as dataset',
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_multilabel_classification_dataset_mock,
              train_predictions=[0, 1, 1],
          ),
          raises(
              ValidationError,
              r"Check requires multi-label classification predictions for "
              r'the "Train" dataset to be of a type sequence\[sequence\[int\]\] that can '
              r"be cast to a 2D numpy array of a shape \(n_samples, n_classes\)",
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_multilabel_classification_dataset_mock,
              train_predictions=[[0], [0, 1], 1],
          ),
          raises(
              ValidationError,
              r"Check requires multi-label classification predictions for "
              r'the "Train" dataset to be of a type sequence\[sequence\[int\]\] that can '
              r"be cast to a 2D numpy array of a shape \(n_samples, n_classes\)",
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_multilabel_classification_dataset_mock,
              train_probabilities=[[0.3, 0.5, 0.2], [0.3, 0.5, 0.2]],
          ),
          raises(
              ValidationError,
              "Check requires multi-label classification probabilities for "
              'the "Train" dataset to have 3 rows, same as dataset',
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_multilabel_classification_dataset_mock,
              train_probabilities=[[1, 1], [0, 0], [0.5, 0.5]],
          ),
          raises(
              ValidationError,
              "Check requires multi-label classification probabilities for "
              'the "Train" dataset to have 3 columns, same as the number of classes',
          ),
      )
      assert_that(
          calling(emtpy_suite.run).with_args(
              train_dataset=text_multilabel_classification_dataset_mock,
              train_probabilities=[
                  [1, 1.2, 1],
                  [0, 0, 0.3],
                  [0.5, 0.2, 0.9],
              ],
          ),
          raises(
              ValidationError,
              r'Check requires multi-label classification probabilities for the "Train" '
              r"to be of a type sequence\[sequences\[float\]\] that can be cast to a 2D numpy "
              r"array of a shape \(n_samples, n_classes\). Each label probability value "
              r"must lay between 0 and 1",
          ),
      )
  
      # Run with no error
      emtpy_suite.run(
          train_dataset=text_multilabel_classification_dataset_mock,
          train_predictions=[[1, 1, 0], [0, 0, 1], [1, 1, 1]],
          train_probabilities=[
              [0.9, 0.8, 0.3],
              [0.9, 0.8, 0.3],
              [0.9, 0.8, 0.3],
          ],
      )
  
file_path: deepchecks/tests/nlp/test_context.py
imports: from hamcrest import assert_that, calling, close_to, raises
  from deepchecks.core.errors import ValidationError
  from deepchecks.nlp import Suite
  from deepchecks.nlp.checks import LabelDrift, SingleDatasetPerformance
label_desc: Use image node_id 2 for calling a global function with example usage: test_wrong_multilabel_prediction_format(text_multilabel_classification_dataset_mock) without return types